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Background: Stress exposure contributes to the onset, maintenance, and recurrence of major depressive disorder (MDD) in adolescents. However, the precise stress facets (e.g. chronicity, domain) most strongly linked to outcomes at different stages along the depression severity continuum remain unclear. Across two studies, chronic and episodic stressors were comprehensively assessed among: (a) healthy youth with (High‐Risk [HR]) and without (Low‐Risk [LR]) a maternal history of MDD and (b) adolescents with current MDD and suicide ideation and healthy controls (HC). Method: Study 1 included LR (n = 65) and HR (n = 22) 12‐ to 14‐year‐olds (49 females; 56.32%) with no lifetime history of mental disorders. Study 2 enrolled 87 mid‐to‐late adolescents (64 females; 73.56%), including 57 MDD youth from a short‐term intensive treatment service and 30 HCs from the community. All depressed youth reported recent suicide ideation; some had no lifetime history suicide attempts (SI; n = 31) and others reported at least one past year attempt (SA; n = 26). The Life Events and Difficulties Schedule was used to capture stressor severity in both studies. Results: We used multiple linear regression models that adjusted for demographic and clinical covariates. Being in the HR versus LR group was associated with more severe chronic (β =.22, CI95 = 0.01–0.42, p =.041), independent (β =.34, CI95 = 0.12–0.56, p =.003), and interpersonal (β =.23, CI95 = 0.004–0.45, p =.047) stress severity. By contrast, the MDD group reported significantly more severe chronic (β =.62, CI95 = 0.45–0.79, p <.001) and dependent (β =.41, CI95 = 0.21–0.61, p <.001) stress than the HC group, but not independent (p =.083) stress. Stress severity did not differ between recent attempters versus youth who reported suicide ideation alone (SA vs. SI contrast). However, the SA group reported a higher rate of targeted rejection events (RR = 3.53, CI95 = 1.17–10.70, p =.026). Conclusions: Our findings clarify the stressor features that may most strongly contribute to adolescent depression and its clinical correlates at two important points along depression's clinical course. [ABSTRACT FROM AUTHOR]

Background: Cross sectional studies have identified linguistic correlates of major depressive disorder (MDD) in smartphone communication. However, it is unclear whether monitoring these linguistic characteristics can detect when an individual is experiencing MDD, which would facilitate timely intervention. Methods: Approximately 1.2 million messages typed into smartphone social communication apps (e.g. texting, social media) were passively collected from 90 adolescents with a range of depression severity over a 12‐month period. Sentiment (i.e. positive vs. negative valence of text), proportions of first‐person singular pronouns (e.g. 'I'), and proportions of absolutist words (e.g. 'all') were computed for each message and converted to weekly aggregates temporally aligned with weekly MDD statuses obtained from retrospective interviews. Idiographic, multilevel logistic regression models tested whether within‐person deviations in these linguistic features were associated with the probability of concurrently meeting threshold for MDD. Results: Using more first‐person singular pronouns in smartphone communication relative to one's own average was associated with higher odds of meeting threshold for MDD in the concurrent week (OR = 1.29; p =.007). Sentiment (OR = 1.07; p =.54) and use of absolutist words (OR = 0.99; p =.90) were not related to weekly MDD. Conclusions: Passively monitoring use of first‐person singular pronouns in adolescents' smartphone communication may help detect MDD, providing novel opportunities for early intervention. [ABSTRACT FROM AUTHOR]

Background: Suicide is a major public health crisis among youth. Several prominent theories, including the Interpersonal Theory of Suicide (IPTS), aim to characterize the factors leading from suicide ideation to action. These theories are largely based on findings in adults and require testing and elaboration in adolescents. Methods: Data were examined from high‐risk 13–18‐year‐old adolescents (N = 167) participating in a multi‐wave, longitudinal study; 63% of the sample exhibited current suicidal thoughts or recent behaviors (n = 105). The study included a 6‐month follow‐up period with clinical interviews and self‐report measures at each of the four assessments as well as weekly smartphone‐based assessments of suicidal thoughts and behaviors. Regression and structural equation models were used to probe hypotheses related to the core tenets of the IPTS. Results: Feelings of perceived burdensomeness were associated with more severe self‐reported suicidal ideation (b = 0.58, t(158) = 7.64, p <.001). Similarly, burdensomeness was associated with more frequent ideation based on weekly smartphone ratings (b = 0.11, t(1460) = 3.41, p <.001). Contrary to IPTS hypotheses, neither feelings of thwarted belongingness, nor interactions between burdensomeness and thwarted belongingness were significantly associated with ideation (ps >.05). Only elevated depression severity was associated with greater odds of suicide events (i.e., suicide attempts, psychiatric hospitalizations, and/or emergency department visits for suicide concerns) during the follow‐up period (OR = 1.83, t(158) = 2.44, p =.01). No effect of acquired capability was found. Conclusions: Perceptions of burdensomeness to others reflect a critical risk factor for suicidal ideation among high‐risk adolescents. Null findings with other IPTS constructs may suggest a need to adopt more developmentally sensitive models or measures of interpersonal and acquired capability risk factors for youth. Refining methods and theoretical models of suicide risk may help improve the identification of high‐risk cases and inform clinical intervention. [ABSTRACT FROM AUTHOR]

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory‐based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real‐world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13–18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1‐week, resulting in indices of emotion (mean‐level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA‐reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real‐world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just‐in‐time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states. This study evaluated relations between a laboratory measure of socioemotional information processing and emotion dynamics captured using smartphone‐based ecological momentary assessment over 1 week in adolescents. Whereas heightened late positive potential (LPP) amplitude for happy faces was related to less happiness inertia, heightened LPP amplitude for angry faces was associated with greater anger inertia. Results highlight potentially important links between laboratory measures and ecologically sensitive measures of affective dynamics, as well as possible targets (e.g., just‐in‐time interventions) for novel treatment approaches. [ABSTRACT FROM AUTHOR]

Background: Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk. Methods: Data come from s everal waves of a prospective cohort study (2016–2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00–19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group. Results: 5454 students ranging in age from 17–36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93–36.07]; Specificity = 97.46 [95% CI 96.21–98.38], PPV = 53.06 [95% CI 40.16–65.56]; AUC range: 0.895 [95% CIs 0.872–0.917] to 0.966 [95% CIs 0.939–0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort. Conclusions: Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Non‐suicidal self‐injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post‐discharge is a high‐risk period for self‐injurious behavior. Thus, identifying predictors that shape the course of post‐discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment.The study included adolescents (N = 612; females n = 435; 71.1%) aged 13–19‐years‐old (M = 15.6, SD = 1.4) undergoing inpatient treatment. Youth were administered clinical interviews and symptom questionnaires at intake (baseline) and before termination. NSSI frequency was assessed at 1‐, 3‐, and 6‐month follow‐ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI across follow‐ups.Three classes were identified: Low Stable (n = 83), Moderate Fluctuating (n = 260), and High Persistent (n = 269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past‐week suicidal ideation (SI) severity, lifetime average and worst‐point SI intensity, and NSSI in the past 30 days (bs = 0.75–2.33). Only worst‐point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class (b = −8.82); no predictors of the Moderate Fluctuating class emerged.This study found a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post‐discharge. Accordingly, this may help identify youth that may benefit from additional monitoring and support post‐hospitalization. [ABSTRACT FROM AUTHOR]

Blunted responses to reward feedback have been linked to major depressive disorder (MDD) and depression risk. Using a monetary incentive delay task (win, loss, break‐even), we investigated the impact of family risk for depression and lifetime history of MDD and anxiety disorder with 72‐channel electroencephalograms (EEG) recorded from 29 high‐risk and 32 low‐risk individuals (15–58 years, 30 male). Linked‐mastoid surface potentials (ERPs) and their corresponding reference‐free current source densities (CSDs) were quantified by temporal principal components analysis (PCA). Each PCA solution revealed a midfrontal feedback negativity (FN; peak around 310 ms) and a posterior feedback‐P3 (fb‐P3; 380 ms) as two distinct reward processing stages. Unbiased permutation tests and multilevel modeling of component scores revealed greater FN to loss than win and neutral for all stratification groups, confirming FN sensitivity to valence. Likewise, all groups had greater fb‐P3 to win and loss than neutral, confirming that fb‐P3 indexes motivational salience and allocation of attention. By contrast, group effects were subtle, dependent on data transformation (ERP, CSD), and did not confirm reduced FN or fb‐P3 for at‐risk individuals. Instead, CSD‐based fb‐P3 was overall reduced in individuals with than without MDD history, whereas ERP‐based fb‐P3 was greater for high‐risk individuals than for low‐risk individuals for monetary, but not neutral outcomes. While the present findings do not support blunted reward processing in depression and depression risk, our side‐by‐side comparison underscores how the EEG reference choice affects the characterization of subtle group differences, strongly advocating the use of reference‐free techniques. Our findings add nuanced event‐related potential (ERP) evidence of blunted versus preserved reward processing in depression and depression risk. Although robust condition effects (win, loss, neutral feedback) were congruent with prior research, our comparative side‐by‐side analysis of ERPs and their current source density transformations using permutation tests and multilevel modeling revealed that the characterization of subtle group differences is impeded and potentially altered by the electroencephalogram reference choice, underscoring the importance of using reference‐free techniques for analyzing ERPs. [ABSTRACT FROM AUTHOR]

Background: Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support., Objective: This 2-arm pilot randomized controlled trial evaluated 2 versions of a digital behavioral health product, Vira (Ksana Health Inc), for their feasibility, acceptability, and preliminary effectiveness in improving mental health in young adults with depressive symptoms and obesity risk factors., Methods: A total of 73 participants recruited throughout the United States were randomly assigned to use Vira either as a self-guided product (Vira Self-Care) or with support from a health coach (Vira+Coaching) for 12 weeks. The Vira smartphone app used passive sensing of behavioral data related to mental health and obesity risk factors (ie, activity, sleep, mobility, and language patterns) and offered users personalized insights into patterns of behavior associated with their daily mood. Participants completed self-reported outcome measures at baseline and follow-up (12 weeks). All study procedures were completed via digital communications., Results: Both versions of Vira showed strong user engagement, acceptability, and evidence of effectiveness in improving mental health and stress. However, users receiving coaching exhibited more sustained engagement with the platform and reported greater reductions in depression (Cohen d=0.45, 95% CI 0.10-0.82) and anxiety (Cohen d=0.50, 95% CI 0.13-0.86) compared to self-care users. Both interventions also resulted in reduced stress (Vira+Coaching: Cohen d=-1.05, 95% CI -1.57 to --0.50; Vira Self-Care: Cohen d=-0.78, 95% CI -1.33 to -0.23) and were perceived as useful and easy to use. Coached users also reported reductions in sleep-related impairment (Cohen d=-0.51, 95% CI -1.00 to -0.01). Moreover, participants increased their motivation for and confidence in making behavioral changes, with greater improvements in confidence among coached users., Conclusions: An app-based intervention using passive mobile sensing to track behavior and deliver personalized insights into behavior-mood associations demonstrated feasibility, acceptability, and preliminary effectiveness for reducing depressive symptoms and other mental health problems in young adults. Future directions include (1) optimizing the interventions, (2) conducting a fully powered trial that includes an active control condition, and (3) testing mediators and moderators of outcome effects., Trial Registration: ClinicalTrials.gov NCT05638516; https://clinicaltrials.gov/study/NCT05638516., (©Lauren S Weiner, Ryann N Crowley, Lisa B Sheeber, Frank H Koegler, Jon F Davis, Megan Wells, Carter J Funkhouser, Randy P Auerbach, Nicholas B Allen. Originally published in JMIR Mental Health (https://mental.jmir.org), 19.09.2024.)

Neurofeedback concurrent with mindfulness meditation may reveal meditation effects on the brain and facilitate improved mental health outcomes. Here, we systematically reviewed EEG and fMRI studies of mindfulness meditation with neurofeedback (mbNF) and followed PRISMA guidelines. We identified 10 fMRI reports, consisting of 177 unique participants, and 9 EEG reports, consisting of 242 participants. Studies of fMRI focused primarily on downregulating the default-mode network (DMN). Although studies found decreases in DMN activations during neurofeedback, there is a lack of evidence for transfer effects, and the majority of studies did not employ adequate controls, e.g. sham neurofeedback. Accordingly, DMN decreases may have been confounded by general task-related deactivation. EEG studies typically examined alpha, gamma, and theta frequency bands, with the most robust evidence supporting the modulation of theta band activity. Both EEG and fMRI mbNF have been implemented with high fidelity in clinical populations. However, the mental health benefits of mbNF have not been established. In general, mbNF studies would benefit from sham-controlled RCTs, as well as clear reporting (e.g. CRED-NF).